Downer has taken the first step in the journey towards predictive maintenance, with the rollout of its data analytics platform, TrainDNA.
Using complex analytics and machine learning, TrainDNA captures data from trains and performs assessments to predict the remaining life of its components.
This solution enables Downer to make data-driven decisions to improve efficiencies in fleet maintenance.
Executive General Manager of Rollingstock Services, Tim Young, said TrainDNA marks a turning point in asset management at Downer.
“This is a data analytics platform on steroids. Analysing such volumes of data will allow our team to establish trends in relative real time, enabling us to proactively predict failures and calculate the remaining life of an asset more effectively.
“The advantage to our customers is that all of this takes place whilst the train is in service without interrupting the operation. At the same time, it enhances worker safety through the potential of removing high-risk inspections,” he said.
Currently deployed on Sydney’s fleet of Waratah trains, TrainDNA will be rolled out across other train fleets Downer maintains over the next 12 months.
“These enhancements in Downer’s asset management capability will boost our ability to better predict failure rates and reduce unscheduled downtimes of the train fleet, resulting in enriched outcomes for our customers and our business.”
Built on the Downer-developed Neuroverse platform, and based on the Microsoft Azure software stack, it was an 18-month process to develop TrainDNA using in-house expertise and a strategic partnership with Deakin University.
Mr Young says the solution is a testament to the skill of the Downer team and its partners.
“TrainDNA demonstrates our capability as a world-class maintainer and asset management partner of choice. While we are still in the early stages of the solution, TrainDNA is a step in the right direction in our journey towards predictive maintenance.”